Bio

Adam White is an Associate Professor of Computing Science at the University of Alberta, a Canada CIFAR AI Chair, a Fellow of the Alberta Machine Intelligence Institute (Amii), and PI of the Reinforcement Learning and Artificial Intelligence Lab. He is also co-founder and Chief Scientific Officer of RLCore, a startup applying reinforcement learning to industrial control. From 2017 to 2023 he was a research scientist at DeepMind. Adam’s research investigates how the problem of intelligence can be modeled as a reinforcement-learning agent continually interacting with an unknown environment, learning from a scalar reward rather than explicit feedback. His group is known for its work on empirical methodology in RL and for pioneering deployments of reinforcement learning in real drinking-water and wastewater treatment plants. He co-created the Coursera Reinforcement Learning Specialization, which has reached over 100,000 learners, and holds a PhD from the University of Alberta.

Research

My research focuses on understanding the fundamental principles of learning in both simulated worlds and industrial control applications. I model intelligence as a reinforcement-learning agent continually interacting with an unknown environment, learning from a scalar reward signal. My group is deeply passionate about good empirical practices and methodologies to determine if our algorithms are ready for deployment in the real world. I have pioneered applications of reinforcement learning to real drinking and wastewater treatment plants and am co-founder of RL Core Technologies, a startup applying AI and machine learning across industrial control.

Keywords: Continual Learning, Reinforcement Learning, Robotics, Knowledge Representation, Intrinsic Motivation

Journal Papers

• Théo Vincent, Kevin Gerhardt, Yogesh Tripathi, Habib Maraqten, Adam White, Martha White, Jan Peters, Carlo D’Eramo (2026). Gradient Iterated Temporal-Difference Learning. Reinforcement Learning Journal.
• Steven Tang, Eric Xiong, Anna Hakhverdyan, Andrew Patterson, Jacob Adkins, Jiamin He, Esraa Elelimy, Parham Mohammad Panahi, Martha White, Adam White (2026). Forager: a lightweight testbed for continual learning with partial observability in RL. Reinforcement Learning Journal.
• Ty Lazar, Matthew Vandergrift, Martha White, Adam White (2026). Revisiting FTA: A Sparse One-to-Many Activation for Reinforcement Learning. Reinforcement Learning Journal.
• Jordan Coblin, Han Wang, Martha White, Adam White (2026). Dynamics Models for Offline Hyperparameter Selection in Real-World RL. Reinforcement Learning Journal.
• Parham Mohammad Panahi, Armin Ashrafi, Hongming Du, Andrew Patterson, Martha White, Adam White (2026). Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning. Reinforcement Learning Journal.
• Samuel Neumann, Jiamin He, Adam White, Martha White (2025). Investigating the Utility of Mirror Descent in Off-policy Actor-Critic. Reinforcement Learning Journal.
• Thomas Ferguson, Alona Fyshe, Adam White (2025). Modelling human exploration with light-weight meta reinforcement learning algorithms. Reinforcement Learning Journal.
• Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Michael Bowling, Martha White (2025). Deep Reinforcement Learning with Gradient Eligibility Traces. Reinforcement Learning Journal.
• Thomas Ferguson, Alona Fyshe, Adam White (2025). Electrophysiological signatures of the effect of context on exploration: Greater attentional and learning signals when exploration is costly. Brain Research.
• Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado, Adam White (2024). AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning. Transactions on Machine Learning Research.
• Andrew Patterson, Samuel Neumann, Martha White, Adam White (2024). Empirical Design in Reinforcement Learning. Journal of Machine Learning Research.
• Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White (2024). Goal-Space Planning with Subgoal Models. Journal of Machine Learning Research.
• Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas (2024). The Cliff of Overcommitment with Policy Gradient Step Sizes. Reinforcement Learning Journal.
• Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White (2024). Investigating the Interplay of Prioritized Replay and Generalization. Reinforcement Learning Journal.
• Edan Jacob Meyer, Adam White, Marlos C. Machado (2024). Harnessing Discrete Representations for Continual Reinforcement Learning. Reinforcement Learning Journal.
• Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White (2024). The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. Reinforcement Learning Journal.
• Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White (2024). Investigating the Properties of Neural Network Representations in Reinforcement Learning. Artificial Intelligence.
• Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White (2023). Reward-respecting subtasks for model-based reinforcement learning. Artificial Intelligence.
• Thomas D. Ferguson, Alona Fyshe, Adam White, Olave E. Krigolson (2023). Humans adopt different exploration strategies depending on the environment. Computational Brain & Behavior.
• Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White (2023). GVFs in the Real World: Making Predictions Online for Water Treatment. Machine Learning.
• Ruo Yu Tao, Marlos C. Machado, Adam White (2023). Agent-State Construction with Auxiliary Inputs. Transactions on Machine Learning Research.
• Matthew Schlegel, Volodymyr Tkachuk, Adam White, Martha White (2022). Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. Transactions on Machine Learning Research.
• Han Wang, Archit Sakhadeo, Adam White, James M. Bell, Vincent Liu, Xutong Zhao, Paul Liu, Tadashi Kozuno, Alona Fyshe, Martha White (2022). No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL. Transactions on Machine Learning Research.
• Andrew Patterson, Adam White, Martha White (2022). A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. Journal of Machine Learning Research.
• Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard S. Sutton, Elliot Ludvig, Adam White (2022). From eye-blinks to state construction: diagnostic benchmarks for online representation learning. Adaptive Behavior.
• Matthew Schlegel, Andrew Jacobsen, Muhammad Zaheer, Andrew Patterson, Adam White, Martha White (2021). General value function networks. Journal of Artificial Intelligence Research.
• Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White (2020). Adapting behaviour via intrinsic reward: A survey and empirical study. Journal of Artificial Intelligence Research.
• Joseph Modayil, Adam White, Richard S. Sutton (2014). Multi-timescale Nexting in a Reinforcement Learning Robot. Adaptive Behavior, 22(2):146–160.
• Shimon Whiteson, Brian Tanner, Adam White (2010). The reinforcement learning competitions. AI Magazine, 31(2):81–94.
• Brian Tanner, Adam White (2009). RL-Glue: Language-independent software for reinforcement-learning experiments. Journal of Machine Learning Research, 10:2133–2136.

Conference Papers

• Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina, Steven Tang, Martha White, Adam White (2025). Position: Lifetime tuning is incompatible with continual reinforcement learning. International Conference on Machine Learning.
• Jacob Adkins, Michael Bowling, Adam White (2024). A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning. Advances in Neural Information Processing Systems.
• Esraa Elelimy, Adam White, Michael Bowling, Martha White (2024). Real-Time Recurrent Learning using Trace Units in Reinforcement Learning. Advances in Neural Information Processing Systems.
• Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas (2024). Position: Benchmarking is Limited in Reinforcement Learning Research. International Conference on Machine Learning.
• David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White (2024). Position: Application-Driven Innovation in Machine Learning. International Conference on Machine Learning.
• Eugene Chen, Adam White, Nathan R. Sturtevant (2023). Entropy as a measure of puzzle difficulty. Artificial Intelligence and Interactive Digital Entertainment.
• Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado (2023). Loss of Plasticity in Continual Deep Reinforcement Learning. Conference on Lifelong Learning Agents.
• Vincent Liu, Han Wang, Ruo Yu Tao, Khurram Javed, Adam White, Martha White (2023). Measuring and Mitigating Interference in Reinforcement Learning. Conference on Lifelong Learning Agents.
• Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White (2023). Auxiliary task discovery through generate-and-test. Conference on Lifelong Learning Agents.
• Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White (2023). The In-Sample Softmax for Offline Reinforcement Learning. International Conference on Learning Representations.
• Samuel Neumann, Sungsu Lim, Ajin George Joseph, Yangchen Pan, Adam White, Martha White (2023). Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. International Conference on Learning Representations.
• Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt (2022). Learning Expected Emphatic Traces for Deep RL. AAAI Conference on Artificial Intelligence.
• Matt McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White (2021). Continual auxiliary task learning. Advances in Neural Information Processing Systems.
• Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt (2021). Emphatic Algorithms for Deep Reinforcement Learning. International Conference on Machine Learning.
• Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White (2020). Gradient Temporal-Difference Learning with Regularized Corrections. International Conference on Machine Learning.
• Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White (2020). Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. International Conference on Autonomous Agents and Multi-Agent Systems.
• Somjit Nath, Vincent Liu, Alan Chan, Adam White, Martha White (2020). Training Recurrent Neural Networks Online by Learning Explicit State Variables. International Conference on Learning Representations.
• Yi Wan, Muhammad Zaheer, Richard S. Sutton, Adam White, Martha White (2019). Planning with Expectation Models. International Joint Conference on Artificial Intelligence.
• Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton (2019). Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. International Conference on Autonomous Agents and Multiagent Systems.
• Andrew Jacobsen, Matthew Schlegel, Cam Linke, Thomas Degris, Adam White, Martha White (2019). Meta-descent for online, continual prediction. AAAI Conference on Artificial Intelligence.
• Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White (2018). Context-dependent upper-confidence bounds for directed exploration. Advances in Neural Information Processing Systems.
• Craig Sherstan, Brendan Bennett, Kenny Young, Dylan Ashley, Adam White, Martha White, Richard S. Sutton (2018). Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. Conference on Uncertainty in Artificial Intelligence.
• Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White (2018). Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains. International Joint Conference on Artificial Intelligence.
• Yangchen Pan, Adam White, Martha White (2017). Accelerated Gradient Temporal Difference Learning. AAAI Conference on Artificial Intelligence.
• Craig Sherstan, Marlos C. Machado, Adam White, Patrick M. Pilarski (2016). Introspective Agents: Confidence Measures for General Value Functions. Artificial General Intelligence.
• Adam White, Martha White (2016). Investigating practical linear temporal difference learning. International Conference on Autonomous Agents and MultiAgent Systems. [Code]
• Martha White, Adam White (2016). A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning. International Conference on Autonomous Agents and MultiAgent Systems.
• Adam White, Joseph Modayil, Richard S. Sutton (2012). Scaling life-long off-policy learning. IEEE International Conference on Development and Learning and Epigenetic Robotics. Paper of distinction award
• Joseph Modayil, Adam White, Patrick M. Pilarski, Richard S. Sutton (2012). Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. IEEE International Conference on Systems, Man, and Cybernetics.
• Joseph Modayil, Adam White, Richard S. Sutton (2012). Multi-timescale Nexting in a Reinforcement Learning Robot. International Conference on Adaptive Behaviour.
• Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, Doina Precup (2011). Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. International Conference on Autonomous Agents and Multiagent Systems.
• Martha White, Adam White (2010). Interval estimation for reinforcement-learning algorithms in continuous-state domains. Advances in Neural Information Processing Systems.
• Nathan R. Sturtevant, Adam M. White (2007). Feature construction for reinforcement learning in hearts. Computers and Games.

Preprints

• Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White (2019). Online Off-policy Prediction. arXiv.

Other Published Works

• Niko Yasui, Sungsu Lim, Cam Linke, Adam White, Martha White (2019). An Empirical and Conceptual Categorization of Value-based Exploration Methods. ICML Exploration in RL Workshop.
• Yangchen Pan, Adam White, Martha White (2017). Accelerated Gradient Temporal Difference Learning. European Workshop on Reinforcement Learning.
• Matthew Schlegel, Adam White, Martha White (2017). Stable predictive representations with general value functions for continual learning. Continual Learning and Deep Networks Workshop, NeurIPS.
• Adam White, Richard S. Sutton (2014). GQ(lambda) Quick Reference Guide.
• Adam White, Joseph Modayil, Richard S. Sutton (2014). Surprise and curiosity for big data robotics. AAAI Workshops.
• Joseph Modayil, Adam White, Patrick M. Pilarski, Richard S. Sutton (2012). Acquiring Diverse Predictive Knowledge in Real Time by Temporal-difference Learning. ERLARS Workshop. Best paper award
• Joseph Modayil, Patrick M. Pilarski, Adam White, Thomas Degris, Richard S. Sutton (2010). Off-policy knowledge maintenance for robots. RSS Workshop.

Theses

• Adam White (2015). Developing a predictive approach to knowledge. Doctoral thesis, University of Alberta.
• Adam White (2006). A standard system for benchmarking in reinforcement learning. Master’s thesis, University of Alberta.

My Students

If you are interested in joining my group as an MSc student, please message me with your transcripts (converted to a 4.0 GPA system) and CV. Admission is based on grades, previous research experience, your research statement, and the quality of your reference letters. All students accepted to our MSc program get guaranteed TA funding. If you would like to work with me, mention my favorite TV show Stargate.

• Jacob Adkins (PhD)
• Armin Ashrafi (MSc)
• Marie Del Valle (MSc)
• Oliver Diamond (MSc)
• Ty Lazar (MSc)
• Baxter Madore (MSc)
• Parham Mohammad Panahi (PhD)
• Samuel Neumann (PhD)
• Sam Scholnick-Hughes (MSc)
• Steven Tang (MSc)
• Eric Xiong (MSc)
• Nathan Zeweniuk (MSc)

Alumni

• Anffany Chen (Postdoc, 2026) — Postdoctoral Fellow, Uhrig Lab, University of Alberta
• Cameron Jen (MSc, 2026)
• Han Wang (PhD, 2025) — Research Scientist, Deeproute.ai
• Tom Ferguson (Postdoc, 2025) — Data Analyst, CASA Mental Health
• Jacob Adkins (MSc, 2025) — PhD student, University of Alberta
• Golnaz Mesbahi (MSc, 2024) — Machine Learning Engineer, Amii
• Parham Mohammad Panahi (MSc, 2024) — PhD student, University of Alberta
• Kevin Roice (MSc, 2024) — Machine Learning Engineer, Amii
• Jordan Coblin (MSc, 2024) — Applied Scientist, ExperienceFlow.ai
• Banafsheh Rafiee (PhD, 2024) — Research Scientist, Spotify
• Matthew Schlegel (PhD, 2023) — Postdoctoral Researcher, University of Calgary
• Eugene Chen (MSc, 2023) — Independent AI & data-visualization creator
• Subhojeet Pramanik (MSc, 2023) — AI Researcher, Softmax
• Edan Meyer (MSc, 2023) — PhD student, University of Alberta
• David Tao (MSc, 2022) — PhD candidate, Brown University
• Samuel Neumann (MSc, 2022) — PhD student, University of Alberta
• Derek Li (MSc, 2022) — Researcher, Huawei Noah’s Ark Lab
• Paul Liu (MSc, 2022) — Software Development Engineer, Amazon
• Sina Ghiassian (PhD, 2022) — Machine Learning Manager, Netflix
• Raksha Kumaraswamy (PhD, 2021) — Research Scientist, Sony AI
• Matt McLeod (MSc, 2021) — Data Scientist, Genentech
• Archit Sakhadeo (MSc, 2021) — Software Engineer, CoinTracker
• Xutong Zhao (MSc, 2021) — PhD student, Mila / Polytechnique Montréal
• Cam Linke (MSc, 2020) — CEO, Amii
• Han Wang (MSc, 2020) — Research Scientist, Deeproute.ai
• Niko Yasui (MSc, 2020) — Machine Learning Resident, Amii
• Andrew Jacobsen (MSc, 2019) — Postdoctoral Researcher, Politecnico di Milano
• Banafsheh Rafiee (MSc, 2018) — Research Scientist, Spotify

Teaching

• Empirical Design in Reinforcement Learning
• Coursera Specialization on Reinforcement Learning
• INT-D 161: AI Everywhere — Fall 2025, Winter 2025, Winter 2024
• CMPUT 655: Reinforcement Learning I — Fall 2022
• CMPUT 365: Introduction to Reinforcement Learning I — Fall 2021
• CMPUT 607: Empirical Reinforcement Learning — Winter 2021
• CMPUT 397: Reinforcement Learning I — Fall 2019
• CMPUT 366: Intelligent Systems — Fall 2017, Fall 2018 · CMPUT 609: Reinforcement Learning — Fall 2017
• CSCI-B 659: Reinforcement Learning for AI — Spring 2016, Spring 2017 (Indiana University)

Media and News

News & Features

• AI pilot project aims to make water treatment greener, more efficient and less expensive. Folio, University of Alberta, October 2020.
• Artificial intelligence provides sustainable solutions for clean drinking water in rural community. CIFAR News, November 2021.
• Edmonton start-up RL Core Technologies to improve drinking-water treatment using data-driven AI. WaterToday.ca, January 2023.
• AI makes a splash in water treatment optimization. Alberta Innovates, March 2024.
• Intelligent water treatment. Filtration+Separation, July 2024.
• Town of Drayton Valley uses AI for water treatment. Alberta Municipalities, July 2024.
• RL Core Technologies closes $5M seed funding. FinSMEs, December 2024. [Official release]
• Optimizing efficiency in water and wastewater management. Alberta Enterprise Corporation, 2025.
• The very boring ways AI is actually changing the world. The Logic, June 2025.
• RLCore unveils RLTune: an adaptive optimization platform for water and wastewater infrastructure. Water Online, June 2026.

Talks, Video & Podcasts

• TD/RL Intro — Deep Learning and RL Summer School. Amii / CIFAR, 2019.
• Better Experiments in RL — Deep Learning and RL Summer School. CIFAR, 2022.
• Continual Subtask Learning. Stanford RL Forum, December 2023.
• Empirical Design in Reinforcement Learning. RL and Agents Reading Group, 2024.
• Approximately Correct podcast — Sidebar episodes. Amii, 2024.
• Outstanding Paper Award Winners @ RLC 2025. TalkRL Podcast, August 2025.

Announcements

• DeepMind expands to Canada with new research office in Edmonton, Alberta. DeepMind, July 2017.
• Meeting society’s AI learning needs. University of Alberta, July 2019.
• Understanding life-long learning in artificial intelligence. University of Alberta, October 2019.
• Five new Canada CIFAR AI Chairs. Amii, December 2019. [2025 renewal]
• AI Everywhere: new AI course at U of A. Amii, September 2023. [The Gateway coverage]
• A shared success: how Amii and the U of A drive Alberta’s AI leadership. Amii, July 2025.

Contact

Office: 7-188 University Commons Building Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2N8